diff --git a/runs/frontier-fidelity-envelope-v1/fixed-pd-pressure-matching-card.md b/runs/frontier-fidelity-envelope-v1/fixed-pd-pressure-matching-card.md
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+++ b/runs/frontier-fidelity-envelope-v1/fixed-pd-pressure-matching-card.md
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+# EXP-SIMFID-Q30-FIXED-PD-PRESSURE: calibrate a nontrivial fixed-shape workload
+
+> Status: planned. This is a real-only workload calibration; its measurements
+> are not part of a Frontier-versus-vLLM winner comparison.
+
+## Question
+
+The former Fixed-PD workload (`2048 -> 128`, 0.215 request/s/GPU) was nearly
+single-request at TP4: its real mean E2E was 604 ms at a TP4 cluster rate of
+0.86 request/s, or about 0.52 in-flight requests by Little's law. It therefore
+does not test the decode batching regime of Trace-PD.
+
+Can a no-prefix, uniform `4096 -> 256` workload be assigned a fixed offered
+rate that reaches the healthy Trace-PD operating point without using a
+simulator result for calibration?
+
+## Controlled calibration
+
+The reference is the real Trace-PD `TP4/MNS64` cell, chosen before this probe
+because it is the real TTFT/E2E winner on that surface:
+
+| Reference metric | Target |
+|---|---:|
+| mean TTFT | 245.95 ms |
+| mean TPOT | 13.18 ms |
+| mean E2E | 44.99 s |
+| offered rate | 0.215 request/s/GPU; 0.86 request/s at TP4 |
+| in-flight proxy | 38.69 requests |
+
+The probe fixes Qwen3-30B-A3B BF16, community vLLM 0.20.0, H20, TP4, MNS64,
+MBT=8192, chunked prefill, and prefix caching off. It compares per-GPU rates
+`{1, 2, 3, 4}` (global TP4 rates `{4, 8, 12, 16}` request/s). Every rate has
+257 exact `4096 -> 256` requests in each of three fresh-server trials; rate
+orders are rotated across trials.
+
+## Decision rule
+
+Pool the three trials for each rate. Among rates with every request completed,
+choose the rate minimizing the Euclidean distance of the two *relative* errors
+for mean TTFT and mean TPOT from the above targets. Report p90 TTFT/TPOT/E2E,
+trial variation, and `global_rate * mean_E2E` as an in-flight proxy, but do not
+turn any of them into an SLO. The selected rate becomes a frozen workload
+contract. The subsequent 12-cell real/simulator surface must be fresh and is
+not allowed to reuse this calibration data.
+
+## Interpretation boundary
+
+This selects a workload regime, not a simulator parameter and not a serving
+capacity point. It may make Fixed-PD more comparable to Trace-PD in batching
+pressure, but it deliberately continues to exclude trace-shaped arrivals and
+prefix reuse; those are separately evaluated workload dimensions.
diff --git a/runs/frontier-fidelity-envelope-v1/run_qwen30_fixed_pd_pressure_probe.sh b/runs/frontier-fidelity-envelope-v1/run_qwen30_fixed_pd_pressure_probe.sh
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+++ b/runs/frontier-fidelity-envelope-v1/run_qwen30_fixed_pd_pressure_probe.sh
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+#!/usr/bin/env bash
+
+# Real-only, pressure-matching probe for the next Fixed-PD workload. This
+# intentionally profiles one anchor, then freezes the workload before any
+# Frontier-vs-real selection comparison.
+
+set -euo pipefail
+
+OUT="${OUTPUT_ROOT:?OUTPUT_ROOT is required}"
+RUNNER_DIR="${RUNNER_DIR:-$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)}"
+CLIENT="${CLIENT:-${RUNNER_DIR}/../frontier-phase-factorial-v0/qwen30_prefill_client.py}"
+VENV_ROOT="${VENV_ROOT:-/tmp/wjh/venvs/vllm-0.20.0-cu129-profiler-v1}"
+MODEL_ROOT="${MODEL_ROOT:-/home/admin/cpfs/wjh/models/Qwen/Qwen3-30B-A3B}"
+FLASHINFER_WORKSPACE_BASE="${FLASHINFER_WORKSPACE_BASE:-${OUT}/flashinfer-workspace}"
+GPU_IDS="${GPU_IDS:-0,1,2,3}"
+TP="${TP:-4}"
+MNS="${MNS:-64}"
+REQUESTS="${REQUESTS:-257}"
+GLOBAL_RATES="${GLOBAL_RATES:-4 8 12 16}"
+SERVER_READY_ATTEMPTS="${SERVER_READY_ATTEMPTS:-180}"
+PORT="${PORT:-8930}"
+SERVED_MODEL="qwen3-30b-fixed-pd-pressure"
+SERVER_PID=""
+
+[[ "${TP}" == "4" ]] || { echo 'ERROR: this calibrated probe is TP4-only' >&2; exit 1; }
+[[ "${MNS}" == "64" ]] || { echo 'ERROR: this calibrated probe is MNS64-only' >&2; exit 1; }
+[[ "${REQUESTS}" =~ ^[1-9][0-9]*$ ]] || { echo 'ERROR: REQUESTS must be positive' >&2; exit 1; }
+[[ -f "${CLIENT}" ]] || { echo "ERROR: client missing: ${CLIENT}" >&2; exit 1; }
+[[ -f "${MODEL_ROOT}/config.json" ]] || { echo "ERROR: model missing: ${MODEL_ROOT}" >&2; exit 1; }
+
+mkdir -p "${OUT}/provenance" "${OUT}/trials" "${FLASHINFER_WORKSPACE_BASE}"
+exec > >(tee -a "${OUT}/controller.log") 2>&1
+
+cleanup_server() {
+  if [[ -n "${SERVER_PID}" ]] && kill -0 "${SERVER_PID}" 2>/dev/null; then
+    kill -TERM -- "-${SERVER_PID}" 2>/dev/null || true
+    for _ in $(seq 1 30); do
+      kill -0 "${SERVER_PID}" 2>/dev/null || break
+      sleep 1
+    done
+    kill -KILL -- "-${SERVER_PID}" 2>/dev/null || true
+  fi
+  SERVER_PID=""
+}
+trap cleanup_server EXIT INT TERM
+
+assert_idle() {
+  nvidia-smi --query-gpu=index,memory.used,utilization.gpu --format=csv,noheader
+  nvidia-smi --query-gpu=memory.used --format=csv,noheader,nounits \
+    | awk '$1 > 16 {exit 1}'
+}
+
+wait_ready() {
+  local target="$1"
+  for _ in $(seq 1 "${SERVER_READY_ATTEMPTS}"); do
+    if curl -fsS --max-time 2 "http://127.0.0.1:${PORT}/v1/models" > "${target}/models.json" 2>/dev/null; then
+      return 0
+    fi
+    if ! kill -0 "${SERVER_PID}" 2>/dev/null; then
+      tail -200 "${target}/server.log" >&2 || true
+      return 1
+    fi
+    sleep 3
+  done
+  echo "ERROR: vLLM did not become ready in $((SERVER_READY_ATTEMPTS * 3)) seconds" >&2
+  return 1
+}
+
+start_server() {
+  local target="$1"
+  export TOKENIZERS_PARALLELISM=false
+  export VLLM_USE_V1=1
+  export TORCH_CUDA_ARCH_LIST=9.0
+  export HF_HUB_OFFLINE=1
+  export TRANSFORMERS_OFFLINE=1
+  export FLASHINFER_WORKSPACE_BASE
+  export HOME=/tmp/wjh
+  export XDG_CACHE_HOME=/tmp/wjh/.cache
+  export VLLM_CACHE_ROOT=/tmp/wjh/.cache/vllm
+  export CUDA_VISIBLE_DEVICES="${GPU_IDS}"
+  setsid "${VENV_ROOT}/bin/vllm" serve "${MODEL_ROOT}" \
+    --host 127.0.0.1 --port "${PORT}" --served-model-name "${SERVED_MODEL}" \
+    --tensor-parallel-size "${TP}" --gpu-memory-utilization 0.92 \
+    --max-model-len 40960 --max-num-batched-tokens 8192 --max-num-seqs "${MNS}" \
+    --no-enable-prefix-caching --enable-chunked-prefill --no-enable-log-requests \
+    > "${target}/server.log" 2>&1 &
+  SERVER_PID=$!
+  wait_ready "${target}"
+}
+
+run_client() {
+  local target="$1" rate="$2"
+  timeout --signal=TERM --kill-after=60s 1800 \
+    "${VENV_ROOT}/bin/python" "${CLIENT}" \
+      --port "${PORT}" --served-model "${SERVED_MODEL}" --model-path "${MODEL_ROOT}" \
+      --rate "${rate}" --requests "${REQUESTS}" --input-tokens 4096 --output-tokens 256 \
+      --timeout-seconds 1200 --output "${target}/result.json"
+}
+
+warmup_server() {
+  local target="$1"
+  timeout --signal=TERM --kill-after=60s 600 \
+    "${VENV_ROOT}/bin/python" "${CLIENT}" \
+      --port "${PORT}" --served-model "${SERVED_MODEL}" --model-path "${MODEL_ROOT}" \
+      --rate 1 --requests 4 --input-tokens 512 --output-tokens 1 \
+      --timeout-seconds 300 --output "${target}/result.json"
+}
+
+analyze() {
+  "${VENV_ROOT}/bin/python" - "${OUT}" "${GLOBAL_RATES}" <<'PY'
+import json
+import math
+import statistics
+import sys
+from pathlib import Path
+
+root = Path(sys.argv[1])
+rates = [float(value) for value in sys.argv[2].split()]
+target = {"ttft_ms": 245.9527667526406, "tpot_ms": 13.178025610291787}
+
+def p90(values):
+    return sorted(values)[math.ceil(0.9 * len(values)) - 1]
+
+rows = []
+for rate in rates:
+    label = f"r{rate:g}"
+    trial_means = {"ttft_ms": [], "tpot_ms": [], "e2e_ms": []}
+    pooled = {key: [] for key in trial_means}
+    for trial in range(1, 4):
+        path = root / "trials" / f"trial{trial}" / label / "result.json"
+        payload = json.loads(path.read_text())
+        workload = payload["workload"]
+        if (float(workload["offered_request_rate"]) != rate or workload["request_count"] != 257
+                or workload["input_tokens"] != 4096 or workload["output_tokens"] != 256
+                or workload["prefix_caching"] is not False):
+            raise ValueError(f"workload drift: {path}")
+        requests = payload["requests"]
+        if len(requests) != 257 or any(not request["success"] for request in requests):
+            raise ValueError(f"incomplete client result: {path}")
+        for key in pooled:
+            values = [float(request[key]) for request in requests]
+            pooled[key].extend(values)
+            trial_means[key].append(statistics.mean(values))
+    row = {
+        "global_rate": rate,
+        "per_gpu_rate": rate / 4.0,
+        "requests_per_trial": 257,
+        "trials": 3,
+        "metrics": {
+            key: {
+                "pooled_mean_ms": statistics.mean(values),
+                "pooled_p90_ms": p90(values),
+                "trial_mean_stdev_ms": statistics.stdev(trial_means[key]),
+            }
+            for key, values in pooled.items()
+        },
+    }
+    row["inflight_proxy"] = rate * row["metrics"]["e2e_ms"]["pooled_mean_ms"] / 1000.0
+    row["relative_distance"] = math.sqrt(sum(
+        ((row["metrics"][key]["pooled_mean_ms"] - target[key]) / target[key]) ** 2
+        for key in target
+    ))
+    rows.append(row)
+
+winner = min(rows, key=lambda row: (row["relative_distance"], row["global_rate"]))
+payload = {
+    "schema": "qwen30-fixed-pd-pressure-probe-v1",
+    "target_trace_pd_tp4_mns64": target,
+    "decision_rule": "minimum Euclidean distance of relative mean TTFT and TPOT errors",
+    "rates": rows,
+    "recommended_global_rate": winner["global_rate"],
+    "recommended_per_gpu_rate": winner["per_gpu_rate"],
+}
+(root / "pressure-analysis.json").write_text(json.dumps(payload, indent=2, sort_keys=True) + "\n")
+lines = [
+    "# Fixed-PD pressure probe",
+    "",
+    "| Global / per-GPU rps | TTFT mean / p90 (ms) | TPOT mean / p90 (ms) | E2E mean / p90 (ms) | In-flight proxy | Relative distance |",
+    "|---|---:|---:|---:|---:|---:|",
+]
+for row in rows:
+    metric = row["metrics"]
+    lines.append(
+        f"| {row['global_rate']:g} / {row['per_gpu_rate']:g} | "
+        f"{metric['ttft_ms']['pooled_mean_ms']:.2f} / {metric['ttft_ms']['pooled_p90_ms']:.2f} | "
+        f"{metric['tpot_ms']['pooled_mean_ms']:.2f} / {metric['tpot_ms']['pooled_p90_ms']:.2f} | "
+        f"{metric['e2e_ms']['pooled_mean_ms']:.2f} / {metric['e2e_ms']['pooled_p90_ms']:.2f} | "
+        f"{row['inflight_proxy']:.2f} | {row['relative_distance']:.3f} |"
+    )
+lines.extend([
+    "",
+    f"Recommended frozen rate: **{winner['global_rate']:g} global rps / {winner['per_gpu_rate']:g} rps per GPU**.",
+    "Selection uses only pooled mean TTFT and TPOT; p90 and in-flight proxy are audit outputs.",
+])
+(root / "pressure-analysis.md").write_text("\n".join(lines) + "\n")
+print(json.dumps(payload, sort_keys=True))
+PY
+}
+
+{
+  echo "FIXED_PD_PRESSURE_PROBE_LAUNCH_ECHO host=$(hostname) model=${MODEL_ROOT} engine=vLLM-0.20.0+cu129 dtype=BF16 config=TP${TP}_MNS${MNS}_MBT8192 gpus=${GPU_IDS} prefix=false shape=4096_to_256 requests_per_rate=${REQUESTS} global_rates={${GLOBAL_RATES}} rate_contract=global_rate_divided_by_TP trials=3 fresh_server=true metric_target=TracePD_TP4_MNS64_meanTTFT245.95ms_meanTPOT13.18ms expected_wall=12-20m expected_cost=0.8-1.4_H20-GPUh output=${OUT}"
+  date -u +START_UTC=%Y-%m-%dT%H:%M:%SZ
+  assert_idle
+  sha256sum "${BASH_SOURCE[0]}" "${CLIENT}" "${MODEL_ROOT}/config.json" > "${OUT}/provenance/input.sha256"
+  "${VENV_ROOT}/bin/vllm" --version > "${OUT}/provenance/vllm.version"
+  "${VENV_ROOT}/bin/python" -c 'import torch, transformers, vllm; print(f"torch={torch.__version__}"); print(f"transformers={transformers.__version__}"); print(f"vllm={vllm.__version__}")' > "${OUT}/provenance/runtime.versions"
+  nvidia-smi --query-gpu=index,name,uuid,driver_version,memory.total --format=csv,noheader > "${OUT}/provenance/gpus.before.csv"
+
+  declare -a ORDERS=("4 8 12 16" "16 12 8 4" "8 16 4 12")
+  for trial in 1 2 3; do
+    trial_root="${OUT}/trials/trial${trial}"
+    mkdir -p "${trial_root}"
+    echo "TRIAL_START trial=${trial} order=${ORDERS[$((trial - 1))]}"
+    start_server "${trial_root}"
+    warmup_server "${trial_root}/warmup"
+    for rate in ${ORDERS[$((trial - 1))]}; do
+      rate_root="${trial_root}/r${rate}"
+      mkdir -p "${rate_root}"
+      echo "RATE_START trial=${trial} global_rate=${rate} per_gpu_rate=$(awk -v value="${rate}" 'BEGIN {printf "%.3f", value / 4}')"
+      run_client "${rate_root}" "${rate}"
+      echo "RATE_COMPLETE trial=${trial} global_rate=${rate}"
+    done
+    cleanup_server
+    assert_idle
+    echo "TRIAL_COMPLETE trial=${trial}"
+  done
+  analyze
+  find "${OUT}" -type f ! -path '*/provenance/artifacts.sha256' -print0 | sort -z | xargs -0 sha256sum > "${OUT}/provenance/artifacts.sha256"
+  nvidia-smi --query-gpu=index,name,uuid,driver_version,memory.total --format=csv,noheader > "${OUT}/provenance/gpus.after.csv"
+  date -u +END_UTC=%Y-%m-%dT%H:%M:%SZ
+  echo 'FIXED_PD_PRESSURE_PROBE_COMPLETE'
+} >> "${OUT}/controller.log" 2>&1